Results on UCF-HMDB. Tab. REF  shows the comparison results of TranSVAE with baselines and SoTA methods on UCF-HMDB.
The best result among all the baselines is highlighted using bold.
Overall, methods using I3D backbone achieve better results than those using ResNet-101.
Our TranSVAE consistently outperforms all previous methods.
In particular, TranSVAE achieves \(93.37\%\)  average accuracy, improving the best competitor CO\(^2\) A [1]}, with the same I3D backbone, by \(1.38\%\) .
Surprisingly, we observe that TranSVAE even yields better results (by a \(2.1\%\)  improvement) than the supervised-target (\(\mathcal {T}_{\text{sup}}\) ) baseline.
This is because that the \(\mathbf {H} \rightarrow \mathbf {U}\)  task already has a good performance without adaptation, i.e., \(88.79\%\)  for the source-only (\(\mathcal {S}_{\text{only}}\) ) baseline, and thus the target pseudo-labels used in TranSVAE are almost correct.
By further aligning domains and equivalently augmenting training data, TranSVAE outperforms \(\mathcal {T}_{\text{sup}}\)  which is only trained with target data.
<FIGURE><TABLE><FIGURE>